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Summary: Empathy, crucial for social interaction, is impaired across various neuropsychiatric conditions. However, the genetic and neural underpinnings of empathy variability remain elusive. By combining forward genetic mapping with transcriptome analysis, we discover that aryl hydrocarbon receptor nuclear translocator 2 (ARNT2) is a key driver modulating observational fear, a basic form of affective empathy. Disrupted ARNT2 expression in the anterior cingulate cortex (ACC) reduces affect sharing in mice. Specifically, selective ARNT2 ablation in somatostatin (SST)-expressing interneurons leads to decreased pyramidal cell excitability, increased spontaneous firing, aberrant Ca2+ dynamics, and disrupted theta oscillations in the ACC, resulting in reduced vicarious freezing. We further demonstrate that ARNT2-expressing SST interneurons govern affective state discrimination, uncovering a potential mechanism by which ARNT2 polymorphisms associate with emotion recognition in humans. Our findings advance our understanding of the molecular mechanism controlling empathic capacity and highlight the neural substrates underlying social affective dysfunctions in psychiatric disorders.

Mobile cranes are essential for transporting heavy materials at construction sites, but their operation carries significant safety risks, particularly due to the potential for overturning accidents. These accidents can be classified into two main categories: mechanical accidents, which are caused by factors such as outrigger failure, excessive load weight, and operator skill, and environmental accidents, which arise from ground subsidence due to groundwater and sinkholes. While numerous studies have addressed the causes and prevention of mechanical accidents, there has been a lack of research focusing on the prevention of environmental accidents. This study presents the development of an Electrical Resistivity Measurement System (ERMS) designed to prevent overturning accidents caused by ground subsidence at mobile crane work sites. The ERMS, mounted on a mobile crane, continuously monitors the ground conditions in real time and predicts the likelihood of ground subsidence to prevent accidents. Unlike typical buried electrode methods, the proposed system features a foldable electrode mechanism and a water supply device, thereby making installation and removal more efficient. Furthermore, it uses a ground stability determination algorithm that qualitatively assesses soft ground conditions, which are the primary cause of ground subsidence. The performance of the ERMS was validated through comparisons with commercial equipment, and its applicability was further confirmed through field tests conducted at mobile crane installations. The ERMS is expected to significantly reduce the risk of accidents caused by ground subsidence during mobile crane operations and to contribute to enhancing overall safety in construction environments.

Category: Trauma; Ankle Introduction/Purpose: Ankle fracture is one of the most common fractures over the age of 50 years. However, there is a lack of research on osteoporotic ankle fractures compared to other osteoporotic fractures. Recently, some studies have been published that Hounsfield Unit(HU) measuring bone attenuation from CT scan is used for assessing the bone density of not only the hip and spine but also extremities such as the distal radius and proximal humerus. This study aims to evaluate the correlation between HU of distal tibia without ankle fracture and DEXA-BMD and determine the cut-off value of HU for osteoporosis diagnosis. Additionally, this study determines whether the HU of the distal tibia obtained from the preoperative CT scan of ankle fractures helps evaluate patients with osteoporotic ankle fractures. Methods: From June 2016 to August 2020, We retrospectively studied foot and ankle traumatic patients who are postmenopausal women or men over 50 years. They conducted a foot or ankle CT scan containing the axial images of the distal tibia and DEXA- BMD within three months. Patients with open fractures with severe displacement or arthritic change were excluded. The mean HU was obtained from two sequential axial images of the distal tibial metaphysis's largest region of interest (ROI), more than 1cm proximal to the tibial plafond. We divided the patients into Group A without ankle fractures and Group B with distal tibia or fibula fractures in the ankle. The correlation analysis was done between the distal tibial HU and DEXA-BMD. We obtained the cut-off value of the distal tibial HU for osteoporosis using receiver operating characteristic(ROC) curves and compared the results between the two groups. Results: Group A was 38 cases(mean age 69.9), and Group B was 59 (mean age 71.0). There were no statistical differences in patients' characteristics, the mean distal tibial HU, bone density, T-score, and rate of osteoporosis in BMD. All HU values in both groups showed excellent inter- and intra-observer reliability(All intraclass coefficients>0.9). Both groups had significant correlations between the distal tibial HU and DEXA-BMD, especially in the femur neck and L3 spine(r=0.706, 0.622, p< 0.001 in Group A, r=0.625,0.499, p< 0.001 in Group B). The cut-off value for osteoporosis of the distal tibial HU was 153.15(sensitivity 73.9%, specificity 86.7%; area under the curve(AUC) 0.841, 95% CI 0.716-0.965, p< 0.001) in Group A. The cut-off value of Group B was 167.35(sensitivity 66.7%, specificity 75.9%; AUC 0.737, 95% CI 0.610-0.865, p=0.002). Conclusion: The distal tibial HU values on the axial images on the CT showed a significant correlation with the central bone of DEXA-BMD. The distal tibial HU value showed excellent reliability with or without fracture. HU value can be easily obtained using a preoperative ankle CT scan without additional cost & radiation exposure to patients with ankle fractures. Therefore, it can help treat osteoporotic ankle fractures by screening for osteoporosis and deciding the fixation method of surgery.

We report enhanced electrical properties of metal–insulator–metal (MIM) capacitors consisting of Al (100 nm)/Nb-doped a-HfO2 (∼30 nm)/Pt (100 nm) on a p-type silicon wafer, where Nb-doped amorphous HfO2 (a-HfO2) layers were deposited by radio frequency magnetron sputtering in various low oxygen partial pressures at room temperature. Polycrystalline HfO2 targets with three different Nb contents of 0 mol. %, 6 mol. %, and 10 mol. % were used in this study. Compared with the leakage current of the undoped a-HfO2 film (∼1.1 × 10−8 A cm−2 at 1 V), greatly reduced leakage currents (∼3.7 × 10−10 A cm−2 at 1 V) with no significant alteration in the dielectric constants (∼22) were obtainable from the MIM samples composed of Nb-doped a-HfO2 films, which is attributable to the suppression of oxygen vacancy formation based on the XPS analysis results. The Nb-doped a-HfO2 dielectric thin films also exhibited improved voltage nonlinearity compared to undoped HfO2. These results indicate that Nb-doped a-HfO2 has potential application as a high-κ dielectric material in MIM capacitors.

This paper considers two important problems for autonomous robot navigation in a dynamic environment, where the goal is to predict pedestrian motion and control a robot with the prediction for safe navigation. While there are several methods for predicting the motion of a pedestrian and controlling a robot to avoid incoming pedestrians, it is still difficult to safely navigate in a dynamic environment due to challenges, such as the varying quality and complexity of training data with unwanted noises. This paper addresses these challenges simultaneously by proposing a robust kernel subspace learning algorithm based on the recent advances in nuclear-norm and l 1 -norm minimization. We model the motion of a pedestrian and the robot controller using Gaussian processes. The proposed method efficiently approximates a kernel matrix used in Gaussian process regression by learning low-rank structured matrix (with symmetric positive semi-definiteness) to find an orthogonal basis, which eliminates the effects of erroneous and inconsistent data. Based on structured kernel subspace learning, we propose a robust motion model and motion controller for safe navigation in dynamic environments. We evaluate the proposed robust kernel learning in various tasks, including regression, motion prediction, and motion control problems, and demonstrate that the proposed learning-based systems are robust against outliers and outperform existing regression and navigation methods.

In this paper, we propose distributed Gaussian process regression (GPR) for resourceconstrained distributed sensor networks under localization uncertainty. The proposed distributed algorithm, which combines Jacobi over-relaxation (JOR> and discrete-time average consensus (DAC), can effectively handle localization uncertainty as well as limited communication and computation capabilities of distributed sensor networks. We also extend the proposed method hierarchically using sparse GPR to improve its scalability. The peiformance of the proposed method is verified in numerical simulations against the centralized maximum a posteriori (MAP) solution and a quick-and-dirty solution. We show that the proposed method outpeiforms the quick-and-dirty solution and achieve an accuracy comparable to the centralized solution. [ABSTRACT FROM AUTHOR]